Machine learning meets genome assembly. (17th August 2018)

• Record Type: Journal Article
• Authors: Padovani de Souza, Kleber; Setubal, João Carlos; Ponce de Leon F. de Carvalho, André Carlos; Oliveira, Guilherme; Chateau, Annie; Alves, Ronnie
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 6(2020)
• Page Start: 2116
• Page End: 2129
• Publication Date: 2018-08-17
• Subjects: machine learning -- genome assembly -- metagenomics -- artificial intelligence -- de novo assembly; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby072
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale. Results: This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers—particularly the ones that use machine learning—to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.